Abstract

Several computational models have been proposed to explain the
mental processes underlying analogical reasoning. However, previous models either
lack a learning component or use limited, artificial data for simulations. To
address these issues, we build a domain-general neural network model that learns
to solve analogy tasks in different modalities, e.g., texts and images.
Importantly, it uses word representations and image representations computed from
large-scale naturalistic corpus. The model reproduces several key findings in the
analogical reasoning literature, including relational shift and familiarity
effect, and demonstrates domain-general learning capacity. Our model also makes
interesting predictions on cross-modality transfer of analogical reasoning that
could be empirically tested. Our model makes the first step towards a
computational framework that is able to learn analogy tasks using naturalistic
data and transfer to other modalities.